Claude Code + Opus 4.5: A Power User's Perspective After Two Years of AI-Assisted Development

I’ve been deep in AI-assisted development since GPT-3.5 dropped. Copilot on day one. ChatGPT API the week it launched. Claude since Sonnet 2. I’ve built workflows, hit walls, found workarounds, and watched the landscape shift underneath me constantly.

When Claude Code landed, I wasn’t excited—I was skeptical. Another wrapper? Another IDE plugin promising magic? I’d seen enough tools come and go.

Eight months later, I’m running Opus 4.5 through Claude Code for nearly everything non-trivial. It’s replaced Postman. It’s transformed how I do TDD. It’s automated workflows I didn’t even realize were bottlenecks.

Here’s what changed, why it matters, and the techniques that separate casual usage from actual leverage.

The Evolution: What Opus 4.5 Fixed

I ran Sonnet hard. Still do for quick tasks. But Sonnet has a ceiling—you feel it when context gets deep, when you’re six files into a refactor, when the problem requires holding multiple competing constraints.

Opus 4.5 raised that ceiling significantly:

Capability	Sonnet 3.5/4	Opus 4.5
Multi-file refactor coherence	Loses thread after ~4 files	Holds across 15+ with consistent patterns
Architectural reasoning	Suggests solutions	Challenges assumptions, proposes alternatives
Edge case identification	Catches obvious ones	Finds the subtle ones you’d hit in prod
Pushback quality	Generic warnings	Specific, reasoned objections with evidence
Test generation depth	Happy paths + obvious edges	Failure modes you’d hit in month 3

The difference isn’t speed—Sonnet is faster. It’s depth. Opus thinks longer before responding, and you feel it in the output quality.

Claude Code: Why Terminal-Native Matters

I burned months on web interfaces and IDE plugins before Claude Code. The friction compounds in ways you don’t notice until it’s gone:

• No copy-paste tax: It reads your files directly
• No context re-establishment: It remembers what you’re working on
• No tool-switching: Stay in terminal, stay in flow
• No file upload limits: Point it at your entire repo

But here’s what most people miss: Claude Code isn’t just convenience. It’s a different relationship with the model. When the AI can see your actual code, your patterns, your naming conventions—it stops being a generic assistant and starts being a collaborator who knows your codebase.

TDD Transformed: Tests Before Code, But Better

I’ve practiced TDD for years. The discipline is valuable, but let’s be honest—writing tests first is friction. You know what you want to build, and writing tests for code that doesn’t exist yet feels like bureaucracy.

Claude Code flipped this completely.

Now my TDD workflow looks like this:

claude "I'm building a TokenBucket rate limiter. Here are my requirements:
- Configurable bucket size and refill rate
- Thread-safe for concurrent access
- Support for burst allowance
- Graceful degradation under memory pressure

Write comprehensive tests first. Cover happy paths, edge cases, concurrency issues, and failure modes. Use pytest. Be adversarial—think about how this breaks in production."

In 30 seconds, I have a test suite that would’ve taken me an hour to write. But here’s the key insight: the tests are better than what I would’ve written.

Why? Because I’m too close to my own implementation ideas. I unconsciously avoid testing the edge cases I’m not sure how to handle. Claude has no such bias. It tests the uncomfortable scenarios.

Then I implement against those tests. Red-green-refactor, but the red phase is instant.

The TDD Feedback Loop

Where this gets powerful is the iteration:

claude "Three tests are still failing. Here's the current implementation and the test output. What am I missing?"

It doesn’t just tell me the fix—it explains why my mental model was wrong. That’s learning compressed into seconds.

I’ve also started doing something I call adversarial TDD:

claude "Here's my implementation. Write tests specifically designed to break it. Find the edge cases I didn't think of."

This catches bugs before they’re bugs. The tests get added to the suite, the implementation gets hardened, and I ship with actual confidence instead of crossed fingers.

Goodbye Postman, You Served Me Well

This one surprised me. Postman was muscle memory—testing APIs, saving collections, sharing with teammates. I never questioned it.

Then I started doing this:

claude "Test the /api/v2/orders endpoint. Here's the OpenAPI spec. I need to verify:
- Authentication flows (valid token, expired token, missing token)
- Pagination (first page, middle page, last page, invalid cursor)
- Filtering (single filter, multiple filters, invalid filter values)
- Error responses (malformed JSON, missing required fields, invalid types)

Run these against localhost:8000. Show me the requests and responses."

Claude Code executes the requests, analyzes the responses, and tells me what’s wrong. Not just “this returned 500”—it tells me why based on the error message and my code.

But here’s where it destroys Postman:

Dynamic test generation: Postman collections are static. Claude generates tests based on the current state of my API. Schema changed? It adapts.

Intelligent assertions: Instead of me writing “status should be 200”, Claude understands what the response should contain based on the request and my business logic.

Contextual debugging: When something fails, it can read my route handlers and tell me where the bug is. Postman just shows you the failure.

No context switching: I’m in my terminal, in my repo, in my flow. Not clicking through a GUI.

I still keep Postman installed. I opened it twice last month—both times to export collections for teammates who haven’t made the switch yet.

The Testing Tools I’ve Replaced

This deserves its own section because the consolidation has been dramatic.

API Testing: Postman → Claude Code

Already covered. The dynamic, context-aware testing is just better.

Integration Testing: Custom Scripts → Claude Code

I used to maintain bash scripts that spun up dependencies, ran test suites, and tore everything down. Now:

claude "Run the integration test suite for the payment module. Spin up the Stripe mock server first. If any tests fail, analyze the logs and tell me what's wrong."

It orchestrates the environment, runs the tests, and debugs failures. One command instead of a 200-line bash script I have to maintain.

Load Testing: k6/Locust Scripts → Claude Code (for exploration)

For serious load testing, I still use dedicated tools. But for quick “can this endpoint handle 100 concurrent requests?” checks:

claude "Hit /api/search with 100 concurrent requests. Vary the query parameters. Show me p50, p95, p99 latencies and any errors."

It’s not a replacement for proper load testing infrastructure, but it’s eliminated 80% of my “let me quickly check this” moments that used to require writing throwaway scripts.

Contract Testing: Pact → Claude Code (partially)

Consumer-driven contract testing is still valuable for cross-team APIs. But for internal services:

claude "Compare the OrderService client expectations in /services/order-client with the actual OrderService API in /api/orders. Flag any mismatches in request/response shapes."

It reads both sides, understands the contract implicitly, and catches drift without the ceremony of maintaining Pact files.

Regression Testing: Manual Checklist → Automated

This one’s huge. I had a mental (sometimes written) checklist of “things to verify before deploying.” Now:

claude "I'm about to deploy the changes in this PR. Based on the diff, what functionality could be affected? Run targeted tests for those areas and tell me if anything looks broken."

It understands the blast radius of my changes and tests accordingly. My regression confidence went from “I think I checked everything” to “I actually checked everything.”

Automating Workflows I Didn’t Know Were Bottlenecks

After two years of AI-assisted development, you start seeing automation opportunities everywhere. Here’s what Claude Code handles that I never would’ve scripted myself:

PR Description Generation

claude "Generate a PR description for the changes in this branch. Include: summary of changes, testing done, areas of risk, and rollback plan if needed."

Takes 10 seconds. Produces better PR descriptions than I write manually when I’m tired and just want to ship.

Dependency Audit

claude "Audit my package.json and requirements.txt. Flag any dependencies with known vulnerabilities, any that are significantly outdated, and any that have better-maintained alternatives."

I used to do this quarterly with manual npm audit and safety check runs. Now it’s part of my weekly workflow because it’s effortless.

Database Migration Review

claude "Review this Alembic migration. Check for: data loss risks, locking issues on large tables, missing indexes for new queries, and rollback safety."

Migration review used to be anxiety. Now it’s a conversation.

Log Analysis

claude "Here are the logs from the last hour showing elevated error rates. Correlate the errors, identify the root cause, and suggest a fix."

This saved me at 2 AM last month. Instead of grepping through logs half-asleep, I dumped them into Claude and got a root cause analysis in minutes.

Code Review Prep

Before I review a teammate’s PR:

claude "Summarize this PR. What's the intent? What are the key changes? What would you flag if you were reviewing this?"

I come into the review already understanding the context. My feedback is better, faster, and more focused.

Deep Techniques I’ve Refined

1. The Context Priming Pattern

Before any complex task, I prime context aggressively:

claude "Read through /src/core and /src/utils. Don't respond yet—just internalize the patterns, naming conventions, and architectural decisions. I'll ask questions next."

This single habit improved output quality by ~40%. The model builds a mental model of your codebase before attempting solutions. Most users skip this and wonder why suggestions feel generic.

2. Adversarial Review Loops

I stopped asking “write X” and started asking “break X”:

claude "Here's my implementation of the rate limiter. Your job is to break it. Find edge cases, race conditions, failure modes I haven't considered. Be adversarial."

Opus excels here. It doesn’t just find bugs—it finds categories of bugs. It’ll identify that your sliding window approach fails under clock skew, that your Redis calls aren’t atomic, that your backoff strategy has a thundering herd problem.

3. The Specification-First Workflow

For any non-trivial feature, I’ve moved to spec-first:

1. Describe the feature in plain English to Claude
2. Ask it to write a technical specification with edge cases
3. Review and refine the spec together
4. Then implement against the spec

This inverts the usual flow. Instead of writing code and discovering edge cases in production, you surface them in the design phase. The implementation becomes almost mechanical.

4. Commit-Level Code Review

I run Claude Code on every significant commit before pushing:

git diff HEAD~1 | claude "Review this diff. Focus on: correctness, performance implications, security concerns, and anything that'll bite us in six months."

Fresh eyes that never get tired.

5. The “Explain Like I’m Auditing” Prompt

When inheriting code or reviewing dependencies:

claude "Explain this module as if you're a security auditor looking for vulnerabilities and an architect evaluating technical debt. Be critical."

The framing matters. Generic “explain this” gets generic explanations. Specific personas get specific, actionable insights.

The CLAUDE.md File (Secret Weapon)

This is underused. Create a CLAUDE.md in your repo root:

# Project Context for Claude

## Architecture
- Monorepo: /api (FastAPI), /web (Next.js), /shared (TypeScript types)
- PostgreSQL + Prisma ORM
- Redis for caching and rate limiting
- Celery for async jobs

## Conventions
- Snake_case for Python, camelCase for TypeScript
- All API endpoints return {data, error, meta} structure
- Errors use custom AppError class with error codes
- Tests mirror source structure in /tests

## Current Focus
- Migrating auth from JWT to session-based
- Performance optimization on /api/search endpoint

## Known Issues
- Rate limiter has race condition under high load (TODO)
- Legacy endpoints in /api/v1 deprecated but still used

## Testing Commands
- `make test` - unit tests
- `make test-integration` - integration tests (requires Docker)
- `make test-e2e` - end-to-end tests (requires running server)

Claude Code reads this automatically. Every conversation starts with context. The productivity gain from this simple file is absurd.

What Actually 10x’d

Let me be specific about where the multiplier lives:

Test writing: What used to be the tedious part of TDD is now the fast part. I write more tests because there’s no friction.

API testing and debugging: Postman + manual debugging → single-command test-and-diagnose cycles.

Debugging complex issues: What used to take a day of printf-debugging now takes an hour. Feed it logs, stack traces, relevant code—it triangulates faster than I can.

Learning new codebases: Onboarding to unfamiliar repos went from weeks to days. I point Claude at the code and have a conversation about architecture before touching anything.

Writing code I’d usually defer: Comprehensive error handling, input validation, edge case coverage. Activation energy used to kill these. Now I actually do them.

Refactoring with confidence: Large refactors used to terrify me. Now I plan them with Claude, execute incrementally, and the test coverage catches regressions before they ship.

Documentation that stays current: Generated from implementation. When code changes, regenerate. No more drift.

The Traps to Avoid

Two years of AI-assisted development taught me what not to do:

Don’t outsource understanding: If you can’t explain what the code does, you can’t maintain it. Use AI to accelerate comprehension, not replace it.

Don’t skip the thinking phase: The temptation is to jump straight to “write me X.” Resist. The planning conversation is where the real value lives.

Don’t trust blindly on security-critical paths: Always review auth, crypto, input handling manually. AI is a second pair of eyes, not a replacement for yours.

Don’t use Opus for everything: It’s slower and more expensive. Sonnet handles 70% of tasks fine. Reserve Opus for complex reasoning, multi-file work, and architectural decisions.

Don’t fight the context window: If you’re constantly re-explaining your project, you’re doing it wrong. Use /add-dir, maintain CLAUDE.md, and let the model build understanding incrementally.

The Compound Effect

Here’s what most “AI productivity” articles miss: the gains compound.

It’s not just that individual tasks are faster. It’s that:

• Faster testing → more tests → fewer bugs → less debugging
• Lower friction → tackling hard problems → better architecture → easier future development
• Automated workflows → more time thinking → better decisions → less rework

Each improvement feeds the next. After eight months, my codebase is cleaner, my test coverage is higher, my deployments are more confident, and my stress levels are lower.

That’s not 10x on a single task. That’s 10x on how I work.

Where This Is Heading

After two years watching this space evolve, patterns are clear:

The gap between “uses AI” and “uses AI effectively” is widening. Tools are getting more capable, but leverage depends on technique. Most developers are still in copy-paste mode while power users are building compound workflows.

Claude Code + Opus 4.5 is the first setup that feels like genuine collaboration rather than sophisticated autocomplete. The model reasons, challenges, and maintains context in ways that earlier iterations couldn’t.

The developers who figure this out now will have a significant advantage. Not because AI writes their code—it doesn’t, really. But because they’ve learned to think with AI, and that changes what’s possible.

The 10x claim? For complex engineering work, it’s underselling it.